Pitch extraction by using autocorrelation function on the log spectrum

Kunieda, Nobuyuki; Shimamura, Tetsuya; Suzuki, Jun

doi:10.1002/(sici)1520-6440(200001)83:1<90::aid-ecjc11>3.3.co;2-0

Cited by 9 publications

(12 citation statements)

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“…where N error and N all are the number of error frames and whole frames, respectively. In evaluation of fine pitch error, standard deviation and mean error is calculated when the ratio of relative errorê eðkÞ is less than 0.05 as shown in some studies [19,20].…”

Section: Conditions For Simulation and Evaluation Methodsmentioning

confidence: 99%

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A pitch detection method based on continuous wavelet transform for harmonic signal

Chisaki

Nakashima

Shiroshita

et al. 2003

Acoust. Sci. & Tech.

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In order to track a rapid transient of pitch, a required frame length of some conventional pitch detection methods is too long. Although there are wavelet based pitch detection methods which require only a few periods of pitch for a frame, they are not robust enough against noise. This paper proposes a new pitch detection method which can work properly under noisy environments even if a frame duration is short. The proposed method consists of a power level detector, a signal analyzer, an autocorrelator, a voiced-unvoiced detector and a lag time interpolator. The signal analyzer is based on the continuous wavelet transform using a harmonic analyzing wavelet. Usage of the harmonic analyzing wavelet gives us more information about a pitch in a scalogram. Simulations of pitch detection for a harmonic chirp signal and speech signals are performed. Performances are compared with two conventional pitch detection methods, cepstrum and modified correlation methods. As a result, a performance of a pitch detection by the proposed method under a noisy environment is better than that of the other two conventional methods. In particular, the largest improvement of performance is obtained for male voices.

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Section: Conditions For Simulation and Evaluation Methodsmentioning

confidence: 99%

“…The condition of simulation for fine pitch error is the same as that for gross pitch error. Standard deviation and mean error for fine pitch error is calculated when the ratio of relative errorê eðkÞ is less than 0.05, as shown in some studies [19,20]. Table 1 shows the result of standard deviation of fine pitch error.…”

Section: Performance Against Noisementioning

confidence: 99%

A pitch detection method based on continuous wavelet transform for harmonic signal

Chisaki

Nakashima

Shiroshita

et al. 2003

Acoust. Sci. & Tech.

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“…(1) The linear estimation coefficient using LPC analysis (2) The sound source amplitude (filter gain) (3) Information to determine whether or not speech is present as expressed by whether the speech in a frame is voiced or unvoiced in each region (4) The fundamental frequency when a frame interval has speech…”

Section: Feature Parametersmentioning

confidence: 99%

Application of the wavelet transform to the low‐bit‐rate speech coding system

Moriai

Hanazaki

2004

Electrical Engineering Japan

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SUMMARYIn the analysis-synthesis coding of speech signals, realization of high quality in the low-bit-rate coding depends on the extraction of its characteristic parameters in the preprocessing. The precise extraction of the fundamental frequency, one of the parameters of the source information, guarantees quality in speech synthesis. But its extraction is difficult because of the influence of the consonant, nonperiodicity of vocal cord vibrations, the wide range of the fundamental frequency, and so on. In this paper we propose a new fundamental frequency extraction with the criterion based on its harmonics structure and low-bitrate speech coding system using the wavelet transform.

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“…Although the pitch is calculated by the cepstrum waveform which is obtained using IFFT, it is known that the fundamental frequency is 90-160 Hz at the case of the adult man and 230-360 Hz at the case of the adult woman. Therefore, in pitch extraction, the area of the pitch selection was defined redundantly in 70-180 Hz at the case of adult man and 210-380 Hz at the case of adult woman in consideration of these fact [5]. …”

Section: Inverse Fft (Ifft)mentioning

confidence: 99%

Emotional speech classification with prosodic prameters by using neural networks

Sato

Mitsukura

Fukumi

et al. 2001

The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001

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Interestingly, in order to achieve a new Human Interface such that digital computers can deal with the KASEI information, the study of the KANSEI information processing recently has been approached. In this paper, we propose a new classification method of emotional speech by analyzing feature parameters obtained from the emotional speech and by learning them using neural networks, which is regarded as a KANSEl information processing.In the present research, KANSEI information is usually human emotion. The emotion is classified broadly into four patterns such as neutral, anger, sad and joy. The pitch as one of feature parameters governs voice modulation, and can be sensitive to change of emotion. The pitch is extracted from each emotional speech by the cepstrum method. Input values of neural networks (NNs) are then emotional pitch patterns, which are time-varying. It is shown that NNs can achieve classification of emotion by learning each emotional pitch pattern by means of computer simulations.

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Pitch extraction by using autocorrelation function on the log spectrum

Cited by 9 publications

References 0 publications

A pitch detection method based on continuous wavelet transform for harmonic signal

A pitch detection method based on continuous wavelet transform for harmonic signal

Application of the wavelet transform to the low‐bit‐rate speech coding system

Emotional speech classification with prosodic prameters by using neural networks

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